New ModelsOpen weights
Qwen3.8-Flash-Next
Qwen3.8-Flash-Next previews the Qwen4 architecture in open weights
Alibaba released open weights for Qwen3.8-Flash-Next: 125B parameters plus a 51B N-gram embedding table with only 6B active, trained at roughly 1/9 the cost of Qwen3.7-Plus while self-reporting DeepSWE 58.7 and SWE-bench Pro 62.5. It previews the Qwen4 architecture: Qwen Sparse Attention (QSA) replaces full attention layers, and the low-bandwidth N-gram embedding table can be offloaded to slow RAM or even an SSD.
125B+51B parameters + N-gram embeddings, 6B active1/9 training cost vs Qwen3.7-Plus58.7 DeepSWE (self-reported); SWE-bench Pro 62.5
New Models
HappyShrimp 1.0
Alibaba's HappyShrimp 1.0 goes end-to-end on music generation
Yes, it's really called HappyShrimp (a shrimp-welfare meme). Alibaba's end-to-end music model generates lyrics, melody, arrangement and vocals in one pass, and unlike the Suno approach it reasons over the prompt first, mapping song structure and harmonic progression before generating audio. Early testers call it a serious and possibly cheapest Suno rival, with 320 free credits at launch. The track played on the show is extremely K-pop.
320 free credits at launch
New ModelsOpen weights
Qwen3.8-27B
Qwen3.8-27B ties GPT-5.6 Luna and runs on a 4090
Alibaba's overnight community darling: a 27B-parameter Apache 2.0 model scoring 52 on the Artificial Analysis Intelligence Index — the same as GPT-5.6 Luna at max reasoning — and 51 on the Agentic index. It runs at ~68 tokens/sec on a 4090, ~40 on Macs via MLX, and even 11 tok/s in-browser on WebGPU kernels. The Hugging Face hub exploded with 152 fine-tunes, 650 quantizations and close to 10 million quant downloads, and Unsloth's 1-bit quants run it on 8GB of RAM at roughly 77% of BF16 quality.
52 AA Intelligence Index, tying GPT-5.6 Luna at max reasoning68 tok/s on a single RTX 4090152 fine-tunes on Hugging Face
New ModelsOpen weights
Wan-Animate-2
Alibaba Wan-Animate-2: 14B character animation under Apache 2.0
Alibaba's Wan team released Wan-Animate-2, a 14B-parameter character animation model under Apache 2.0. It wins over 70% of blind preference comparisons.
14B parameters70%+ blind preference win rate
New Models
Wan 3.0
Wan 3.0 drops into public beta live during the show, with native 30-second generation
Alibaba's Tongyi Lab pushed Wan 3.0 into public beta minutes before ThursdAI went live: native 30-second single-shot generation and Omni-Reference conditioning on text, images, audio, and video together. Given the Wan line's open-weight track record, this is the drop the open source community most wants weights for.
30s native single-shot generation4 reference modalities via Omni-Reference
New Models
Qwen3.8-Max
Qwen3.8-Max: Alibaba's 2.4T-parameter flagship, with open weights promised within a week
Alibaba's flagship MoE arrives via API: 2.4T total parameters, 95B active, 1M context, at $2/$6 per million tokens, with open weights plus a 27B sibling promised for the week of August 10, the first Max-class Qwen slated for release. It ranks #2 on EyeBench for vision behind only OpenAI's Sol, and the oh-my-cli demo ran 16 days of fully autonomous coding: 265 commits, 127 PRs, 151 issues, zero human intervention. Nisten's hands-on: best-in-class visual data labeling. Yam's counter: other frontier models pass those tests too, and the 27B is the one you'll run at home.
2.4T / 95B total / active parameters#2 EyeBench vision rank, behind only Sol16 days autonomous run: 265 commits, 127 PRs
